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Haynes, James A.

Publications and source records attributed to Haynes, James A..

25 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. Further, the performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterization of the Benefit of APS Flash Coatings in Improving TBC Lifetime

The addition of an air plasma sprayed (APS) “flash” layer on top of a high velocity oxygen fuel (HVOF) bond coating has been shown to extend the lifetime of thermal barrier coatings. A series of furnace cycle tests (FCTs) has been conducted at 1100 °C in air + 10% H 2 O to study the benefit of flash coatings on rod and disk alloy 247 specimens and provide a better mechanistic understanding of their benefit. Flash coatings of NiCoCrAlY and NiCoCrAlYHfSi both improved the FCT lifetime of rod specimens tested in 100-h cycles and disk specimens tested in 1-h cycles. In 1-h cycles, the NiCoCrAlY flash coating significantly outperformed an HVOF-only NiCoCrAlYHfSi bond coating and a NiCoCrAlYHfSi flash coating. Both flash coatings increased the bond coating roughness compared to HVOF. During exposure, the flash layer formed an intermixed alumina-metal layer that appeared to inhibit crack formation. Using a time series of observations, the lower Y + Hf content in the Y-only flash coating appeared to reduce Al consumption. The HVOF layer acted as a source of Al for the adjacent mixed zone. A second series of specimens included a fully APS bond coating where oxide had penetrated through the entire coating to the substrate after only 100, 1-h cycles and lifetime was similar to an HVOF-only bond coating. The inner HVOF layer with the outer APS flash coating prevented this complete penetration from occurring.

Pint, Bruce↗
DOE OSTI2020

Solute-vacancy clustering in aluminum

In this study, we present an extensive first-principles database of solute-vacancy, homoatomic, heteroatomic solute-solute, and solute-solute-vacancy binding energies of relevant alloying elements in aluminum. We particularly focus on the systems with major alloying elements in aluminum, i.e., Cu, Mg, and Si. The computed binding energies of solute-vacancy, solute-solute pairs, and solute-solute-vacancy triplets agree with available experiments and theoretical results in literature. We consider physical factors such as solute size and formation energies of intermetallic compounds to correlate with binding energies. Systematic studies of the homoatomic solute-solute-vacancy and heteroatomic (Cu, Mg, or Si)-solute-vacancy complexes reveal the overarching effect of the vacancy in stabilizing solute-solute pairs. The binding energy database presented here elucidates the interaction between solute cluster and vacancy in aluminum, and it is expected to provide insight into the design of advanced Al alloys with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An additively manufactured AlCuMnZr alloy microstructure and tensile mechanical properties

In this study, selective laser melting (SLM) was used to fabricate an AlCuMnZr alloy. The microstructural features that resulted from additive manufacturing (AM) were significantly refined compared to the corresponding cast alloy features. A combination of fine equiaxed and columnar grains along with in-situ formation of θ' precipitates during AM leads to enhanced yield strength (up to 300°C) in the as-fabricated AM alloy. The refinement of brittle intermetallics and a bimodal grain size distribution leads to improved tensile elongation in the AM alloy. The results illustrate the microstructural advantages that can result from additive processing over conventionally processed microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Grain Refinement Effect on the Hot-Tearing Resistance of Higher-Temperature Al–Cu–Mn–Zr Alloys

The hot-tearing resistance of Al-Cu-Mn-Zr (ACMZ) alloys was investigated as a step toward introducing these new cast alloys for severe duty, higher-temperature applications, such as cylinder heads for down-sized, turbocharged automotive engines. Alloy Cu compositions were varied from 5 to 8 wt.%. Targeted Ti levels were 0.02, 0.1, and 0.2 wt.% via additions of the Al–5Ti–1B master alloy. Hot-tearing resistance was assessed by visual examination and ranking of the cracking severity in a multi-arm permanent mold casting. It was found that at high impurity contents (Fe and Si of 0.2 wt.% each), the Al–Cu–Mn–Zr alloy with 4.95 wt.% Cu exhibited the poorest hot-tearing resistance, irrespective of the grain refining amount. Microstructural analysis indicated an effective reduction in the grain size, as the Ti additions were increased to 0.02 and 0.1 wt.% Ti via the Al–Ti–B grain refiner. The finest grain size was attained with a 0.1 wt.% Ti. Based on the hot-tearing evaluation, it was found that the additional grain refining via the Al–5Ti–1B master alloy at 0.1 wt.% Ti significantly reduces the hot-tearing susceptibility at Cu contents greater than 7.3 wt.% for ACMZ alloys with low Fe and Si. These findings indicate that the best hot-tearing resistance was observed at a grain refiner level of 0.1 wt.% Ti and high Cu content (greater than 7.3 wt.%). This study to indicates that these Al–Cu–Mn–Zr alloys, which possess excellent microstructural stability and mechanical properties at elevated temperatures, can also possess excellent hot-tearing resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗